Five Best Practices for Big Data Analytics

Business is awash in data—and also big data analytics programs meant to make sense of this data and apply it toward competitive advantage. A recent Gartner study found that more than 75 percent of businesses either use big data or plan to spin it up within the next two years.

Not all big data analytics operations are created equal, however; there’s plenty of noise around big data, but some big data analytics initiatives still don’t capture the bulk of useful business intelligence and others struggling getting off the ground.

For those businesses currently struggling with the data, or still planning their approach, here are five best practices for effectively using big data analytics.

1. Start at the End

The most successful big data analytics operations start with the pressing questions that need answering and work backwards. While technology considerations can steal the focus, utility comes from starting with the problem and figuring out how big data can help find a solution.

There are many directions that most businesses can take their data, so the best operations let key questions drive the process and not the technology tools themselves.

“Businesses should not try to boil the ocean, and should work backwards from the expected outcomes,” says Jean-Luc Chatelain, chief technology officer for Accenture Analytics, part of Accenture Digital.

2. Build an Analytics Culture

Change management and training are important components of a good big data analytics program. For greatest impact, employees must think in terms of data and analytics so they turn to it when developing strategy and solving business problems. This requires a considerable adjustment in both how employees and businesses operate.

Training also is key so employees know how to use the tools that make sense of the data; the best big data system is useless if employees can’t functionally use it.

“We approach big data analytics programs with the same mindset as any other analytic or transformational program: You must address the people, process and technology in the organization rather than just data and technology,” says Paul Roma, chief analytics officer for Deloitte Consulting.

“Be ready to change the way you work,” adds Luc Burgelman, CEO of NGDATA, a firm that helps financial services, media firms and telecoms with big data utilization. “Big data has the power to transform your entire business but only if you are flexible and prepared to be open to change.”

3. Re-Engineer Data Systems for Analytics

An increasing range and volume of devices now generate data, creating substantial variation both in sources and types of data. An important component of a successful big data analytics program is re-engineering the data pipelines so data gets to where it needs to be and in a form that is useful for analysis. Many existing systems were not developed for today’s big data analysis needs.

“This is still an issue in many businesses, where the data supply chain is blocked or significantly more complex than is necessary, leading to ‘trapped data’ that value can’t be extracted from,” says Chatelain at Accenture Digital. “From a data engineering perspective, we often talk about re-architecting the data supply chain, in part to break down silos in where data is coming from, but also to make sure insights from data are available where they are relevant.”

4. Focus on Useful Data Islands

There’s a lot of data. Not all of it can be mined and fully exploited. One key of the most successful big data analytics operations is correctly identifying which islands of data offer the most promise.

“Finding and using precise data is rapidly becoming the Holy Grail of analytics activities,” says Chatelain. “Enterprises are taking action to address the challenges present in grappling with big data, but [they] continue to struggle to identify the islands of relevant data in the big data ocean.”

Burgelman at NGDATA also stresses the importance of data selection.

“Most companies are overwhelmed by the sheer volume of the data they possess, much of which is irrelevant to the stated goal at hand and is just taking up space in the database,” he says. “By determining which parameters will have the most impact for your company, you’ll be able to make better use of the data you have through a more focused approach rather than attempting to sort through it all.”

5. Iterate Often

Business velocity is at an all-time high thanks to more globally connected markets and rapidly evolving information technology. The data opportunities are constantly changing, and with that comes the need for an agile, iterative approach toward data mining and analysis. Good big data analytics systems are nimble and always iterating as new technology and data opportunities emerge.

Big data itself can help drive this evolution.

“One of the amazing things about big data analytics is that it can help organizations gain a better understanding of what they don’t know,” says Burgelman. “So as data comes in and conclusions are reached, you’ve got to be flexible and open to changing the scope of the project. Don’t be afraid to ask new questions of your data on an ongoing basis.”

The importance of effective big data use grows by the day. This makes analytics best practices all the more important, and these five top the list.

About the Author

Peter Kowalke is journalist and editor who has been covering business, technology and lifestyle trends for more than 20 years. When not writing, he runs Kowalke Relationship Coaching.

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